# How to use a Righthand with Hugging Face

Use Righthand to prepare a Hugging Face model-card comparison for a defined research task.

By Righthand Team · 2026-10-12

Source: https://www.righthand.ai/blog/how-to-use-righthand-with-hugging-face

## Quick answer

Use Righthand to prepare a Hugging Face model-card comparison for a defined research task. Compare stated scope and limitations before any deployment decision.

Start with a reviewable deliverable, keeping Hugging Face as the accepted home for its records. This guide uses an explicitly approved snapshot or browser read; it does not assume that every provider API action is exposed by your Righthand connection.

## Evidence and access

Hugging Face documents model cards, metadata, datasets, and task tags. See the [official Hugging Face reference](https://huggingface.co/docs/hub/model-cards). Provider capabilities and Righthand's available tools are separate checks.

Use the approved model-card or inference-output packet with repository identity, revision, license reference, task, and owner-provided evaluation criteria. Verify the required tools and owner-approved scope through [Righthand integrations](/integrations); use the supplied packet when a native read is unavailable.

## Define the objects and authority

Include these fields in the working input: repository ID, revision, task tag, license reference, stated training data, limitations, evaluation source. These are the requested review columns, not a promise that every provider endpoint returns them under those exact names.

A model card is provider or author documentation, not an independent guarantee. Licensing and deployment decisions require qualified owner review.

| Responsibility | Owner |
| --- | --- |
| Prepare source scope and access | Account owner |
| Organize evidence and proposed next steps | Righthand |
| Approve consequential changes | the ML lead |

## Illustrative first-run brief

> Use the owner-provided Hugging Face snapshot or approved read-only view. Compare three approved model cards for document classification. Extract intended tasks, stated limits, and missing evaluation details. Do not download weights or run inference. Run once for the agreed sample, using America/Los_Angeles for the review deadline and preserving source timestamps separately. Return the draft in this conversation by Friday at 3 PM Pacific for the ML lead to review. Include source references and missing fields. Do not write records, spend money, change permissions, or send external messages. Stop and report an unavailable source rather than substituting another account.

## Expected result and verification

One card may omit a relevant evaluation measure. The comparison marks that gap instead of inventing equivalent performance.

Open the referenced revision and check each quoted limitation against the original card. Record the source cutoff, applied filters, and items excluded from the sample. The reviewer should be able to follow each consequential finding back to an original object. A well-written summary is not evidence that a proposed action happened. If a later write is authorized, verify its specific result separately before changing the task's status.


### Illustrative input and draft output

These synthetic entries show the requested review, not live account results or a provider field schema.

| Source item | Supplied observation | Draft conclusion |
| --- | --- | --- |
| Model A | Revision and task supplied | Review documented fit |
| Model B | Evaluation evidence absent | Performance unverified |
| Output C | Different revision | Separate result |

## Common failure and recovery

A repository's default branch can change. Preserve revision identity and recheck changed documentation before adopting a model.

When the repository revision is unspecified, request the exact reference before comparing documentation or behavior; a moving name cannot reproduce the review.

## FAQ

### What should the reviewer verify first?

Verify repository, revision, intended task, supplied license context, and actual evaluation evidence. A model card describes claims and intended use, not your deployment's measured result.

### How should a repeat run be scoped?

Keep revision and evaluation-input references stable. Re-review changed cards or outputs and leave license interpretation and deployment approval with the appropriate owners.

For connection and permission details, use [Righthand Docs](https://docs.righthand.ai). Review [Pricing](/pricing) alongside any separate provider charges before expanding the workflow.